第一届 Learning and Mining with Noisy Labels 竞赛 大家好,我们在 IJCAI-ECAI 2022的workshop中正在举办带噪学习的比赛。“带噪”指的是训练数据含有标签噪声,比如一张“猫”的图片被误标记成“狗”。带噪学习的任务是在含有标签噪声的数据中训练一个对噪声稳健的模型以及检测数据中的带噪数据。 我们举办这一...
我们提出了邻居一致性正则化(Neighbor Consistency Regularization),这是一种新的损失项,用于深度学习,带有噪声标签,鼓励具有相似特征表示的示例具有相似的预测。 我们通过经验验证,在合成和真实分布的大范围噪声水平下,NCR比几个重要基线获得了更好的精度,并且是混合[48]的流行正则化技术的补充。 我们证明,NCR在评估合...
通过prune, count, rank 3 步可以高效率算出 joint probabilities(true and predicted labels) 根据joint probabilities 识别 label error。 理论基础是 Angluin 1988 的 CNP 理论。 本文的核心贡献: we prove CL exactly estimates the ...
第一种 Clean Data 是比较容易获取的，可以随便找现有的公开数据集，通过模拟置噪的方式来使数据集变成...
基本信息 \1.标题:DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING \2.作者:Junnan Li, Richard Socher, Steven C.H. Hoi \3.作者单位:Salesforce Research \4.发表期刊/会议:ICLR \5.发表时间:2020 \6.原文链接:https://arxiv.org/abs/2002.07394 ...
This repository is the official implementation ofAsymmetric Loss Functions for Learning with Noisy Labels[ICML 2021] andAsymmetric Loss Functions for Noise-tolerant Learning: Theory and Applications[T-PAMI]. Requirements Python >= 3.6, PyTorch >= 1.3.1, torchvision >= 0.4.1, numpy>=1.11.2, tqdm...
Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data....
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label...
2.1 LEARNING WITH NOISY LABELS 现有的训练带噪声标签的dnn的方法大都是为了修正loss函数。修正方法可以分为两类。第一种方法对所有样本一视同仁,通过重新标记噪声样本来显式或隐式地纠正损失。对于重标记方法,对噪声样本的建模采用有向图模型(Xiao et al., 2015)、条件随机场(Vahdat, 2017)、知识图(Li et al...
Deep learning with noisy labels: exploring techniques and remedies in medical image analysisLabel noiseDeep learningMachine learningBig dataMedical image annotationSupervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical ...